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Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

preprint
revised on 04.03.2019, 12:19 and posted on 04.03.2019, 16:45 by Qiannan Duan, Jianchao Lee, Jinhong Gao, Jiayuan Chen, Yachao Lian, Zoudi Wang, Can Wang, Zhaoyi Xu, Juan Ren, Sifan Bi

Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.

Funding

This work is supported by the National Natural Science Foundation of China (No.50309011) and the Scientific Research Foundation for the Returned Overseas Chinese Scholars (08501041585).

History

Email Address of Submitting Author

qiannanduan@smail.nju.edu.cn

Institution

School of the Environment, Nanjing University

Country

China

ORCID For Submitting Author

0000-0002-9781-1066

Declaration of Conflict of Interest

The authors declare no competing interests.

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